{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/estimating-multi-year-247-origin-destination","title":"Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data","arxiv_id":"1901.09266","date":"2019-01-26","proceeding":null,"authors":["Wei Ma","Zhen","Qian"],"abstract":"Dynamic origin-destination (OD) demand is central to transportation system\nmodeling and analysis. The dynamic OD demand estimation problem (DODE) has been\nstudied for decades, most of which solve the DODE problem on a typical day or\nseveral typical hours. There is a lack of methods that estimate high-resolution\ndynamic OD demand for a sequence of many consecutive days over several years\n(referred to as 24/7 OD in this research). Having multi-year 24/7 OD demand\nwould allow a better understanding of characteristics of dynamic OD demands and\ntheir evolution/trends over the past few years, a critical input for modeling\ntransportation system evolution and reliability. This paper presents a\ndata-driven framework that estimates day-to-day dynamic OD using high-granular\ntraffic counts and speed data collected over many years. The proposed framework\nstatistically clusters daily traffic data into typical traffic patterns using\nt-Distributed Stochastic Neighbor Embedding (t-SNE) and k-means methods. A\nGPU-based stochastic projected gradient descent method is proposed to\nefficiently solve the multi-year 24/7 DODE problem. It is demonstrated that the\nnew method efficiently estimates the 5-minute dynamic OD demand for every\nsingle day from 2014 to 2016 on I-5 and SR-99 in the Sacramento region. The\nresultant multi-year 24/7 dynamic OD demand reveals the daily, weekly, monthly,\nseasonal and yearly change in travel demand in a region, implying intriguing\ndemand characteristics over the years.","url_abs":"http://arxiv.org/abs/1901.09266v1","url_pdf":"http://arxiv.org/pdf/1901.09266v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"estimating-multi-year-247-origin-destination","repo_url":"https://github.com/Lemma1/DPFE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}